class_docstr: >>> LitClassifier(Backbone())  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
  LitClassifier(
    (backbone): ...
  )
code: def __init__(
      self,
      backbone: Optional[Backbone] = None,
      learning_rate: float = 0.0001,
  ):
      super().__init__()
      self.save_hyperparameters(ignore=["backbone"])
      if backbone is None:
          backbone = Backbone()
      self.backbone = backbone
  
imports: from os import path
  from typing import Optional
  import torch
  from lightning.pytorch import LightningDataModule, LightningModule, cli_lightning_logo
  from lightning.pytorch.cli import LightningCLI
  from lightning.pytorch.demos.mnist_datamodule import MNIST
  from lightning.pytorch.utilities.imports import _TORCHVISION_AVAILABLE
  from torch.nn import functional
  from torch.utils.data import DataLoader, random_split
label_desc: Use image node_id 1 to create a new LitClassifier object from inherited base classes: LightningModule with example: obj = LitClassifier(backbone, learning_rate)